Comment by rscho
5 years ago
What people often miss with J is how good it is for exploratory programming due to its terseness and most importantly its integrated columnar store. It replaced R for data wrangling in my stats programs, and relegated it to just running the models.
Once you get used to it, it's really a breeze and far easier to modify than 500+ sloc R scripts.
Of course, that does not work for everyone, and especially not for big teams, but still!
> It replaced R for data wrangling in my stats programs
Do you find it faster?
I have good experiences with J due to its terseness, but I'm curious why scientists still use R/Python - is it just inertia? Libraries/FFI?
Faster to program yes, massively so. Faster to execute, it depends on what I'm doing, but it's never a problem for me as my data is usually pretty small and quite messy. It doesn't feel slow at all, at least. And obviously if you're doing arrays you'll certainly not run faster in Python/R.
As for R/Python it's mostly familiarity with the notation (especially for python) and established popularity, with a large ecosystem as a consequence.
I mean, as a beginner in Python it just works (slowly). As a beginner in J, you cry... The interesting distinction is that Python/R APIs can be quite convoluted and the rug may be pulled from under you without warning, while in J you learn the primitives and you're off to the races. Also, J is much faster for its use case and avoids the need to write C in most cases where using it is relevant.